acmt board review course population health and assessments jeffrey brent, m.d., ph.d. toxicology...

Download ACMT Board Review Course Population Health and Assessments Jeffrey Brent, M.D., Ph.D. Toxicology Associates University of Colorado School of Medicine

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  • Slide 1
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  • ACMT Board Review Course Population Health and Assessments Jeffrey Brent, M.D., Ph.D. Toxicology Associates University of Colorado School of Medicine and Colorado School of Public Health
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  • Topics for this Lecture 1.Exposure monitoring and sampling 2.PPE 3.Study designs and measures of association 4.Statistical concepts 5.Bias and confounding 6.The Hill criteria 7.Sensitivity, specificity, predictive values
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  • Exposure monitoring and sampling Exposure monitoring Environmental sampling: Wipe sampling Water sampling Air sampling Breathing zone measurements are best for inhalational exposures Biological monitoring e.g.: Blood Pb Urine mercury
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  • Personal protective equipment Respiratory Chemically protective clothing
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  • Respiratory protection Classification by size Quarter face Half face Full face Classification by function Air-purifying Uses chemical specific cartridges Supplied air SCBA
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  • Protection Factor The factor by which exposure is reduced by use of a respirator Ambient/protection factor = exposure For example: Ambient of 100 PPM Protection factor of 10 Exposure = 100/10 = 10 PPM Protection factors range form 5 10,000 The goal is to get exposure to below safe limits
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  • Chemically protective clothing Simple protection Ex. Aprons, boots, gloves Nonencpasulating suits 1 or 2 pieces, for example: 1 piece hooded coveralls Hooded jacket + chem protective pants Encapsulating suit Highest level of protection
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  • Chemically protective clothing is usually designated by the EPA rating system Level A = Max protection Encapsulating suit SCBA Level B Supplied air respirator (or SCBA) Non-encapsulating garment Level C Air purifying respirator Non-encapsulating garment Level D = standard work clothes
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  • STUDY DESIGN AND MEASURES OF ASSOCIATION STATISTICAL CONCEPTS NEXT
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  • Types of human data Anecdotal Case-reports and series Controlled observational Controlled epidemiological studies Controlled interventional Trials
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  • Controlled observational studies Cohort Cross-sectional Mortality Case-control Ecologic For all epi studies: Groups should be matched for relevant variables (e.g.) Age Sex Anything else that can affect results
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  • Cohort studies Compares exposed group to an unexposed group Can be retrospective or prospective Can assess incidence rates Incidence = Rate of new cases (e.g. Cases/100,00/yr) Prevalence = Number of cases in the population (e.g. cases/100,000) Results expressed as Relative Risk (aka risk ratio or rate ratio)
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  • Cross-sectional studies Compares exposed group to an unexposed group at one snapshot in time Provides prevalence data Example: Prevalence of drug abuse in medial toxicologists taking the board exam v those that are not Results expressed as Relative Risk (aka risk ratio or rate ratio)
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  • Mortality Studies Typically a variation of a cohort study Assesses diagnoses at time of death Results expressed as mortality rates corrected for relevant factors (Standardized mortality rates) Usually expressed as a percentage (Mortality rate of exposed/rate in unexposed) X 100 = SMR) Thus an SMR of 100 = no difference btw exposed and unexposed
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  • Case-control studies Compares individuals with a specific condition with individuals that do not have that condition and compares exposures (or other risk factors) For example: Comparing medical toxicologists with alcohol abuse (the cases) with those w/o this dx (controls) to see if there is a higher likelihood of alcoholic abuse if preparing to take the boards. Thus assesses risk factors (e.g. exposures) related to specific conditions Recall bias major problem Results expressed as Odds Ratios
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  • Ecologic Studies Assesses population numbers, not individuals Example: Rate of admission for asthma exacerbations in a city with high airborne PM 10 compared to a city with low PM 10 Results expressed as Relative Risk (aka risk ratio or rate ratio) Ex: Snows study of cholera rates in London districts
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  • Assessment of results of epi studies By convention a result is statistically significant if the likelihood that it is chance result is < 5% or approx 2SDs from the mean
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  • Interpretation of EPI Data You can never assess the degree of association based only on the magnitude of the RR, OR or SMR These values have an inherent uncertainty that is determined by the nature of the data In modern epi this uncertainty is expressed as Confidence Intervals
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  • The 95% Convention In science the uncertainty in a result is expressed as that range of data in which there is a 95% likelihood that the real value exists CIs express this range Ex: RR 1.6 (CI 0.7 2.4)
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  • The importance of confidence intervals If RR 1.6 (0.7 2.4) Than there is a 95% likelihood that the real value lies between 0.7 2.4 If the real RR is: >1= association 1= Non-association
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  • What about p values? p Values are an older way of describing statistical significance P < 0.05 means a result is statistically significant OR 1.6 (0.7 2.4) = OR 1.6 p > 0.05 OR 1.6 (1.1 2.1) = OR 1.6 p < 0.05
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  • Now the Bad News A statistical relationship never a priori means a causal relationship Does epi data show a statistically significant relationship? No No established association Yes Does the statistical association mean a causal relationship?
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  • It is not the falling of the leaves that causes winter to come There are many more statistical associations in toxicology than there are causal relationships
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  • How to get from association to causation Requires specific rigorous methodology Stems from Doll and Hills observation of an association between smoking and lung cancer
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  • Hills Viewpoints To be applied if a statistical association is shown to exist Does not account for quality of studies showing such an association
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  • Hills Viewpoints Strength of association Consistency Specificity Biological gradient Temporal precedence Coherence Plausibility Experimental support Analogy Also must consider the quality of the study
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  • Bias and confounding Bias = systematic error Ex: You are doing a study on childhood bl Pb concentrations and behavior. However, your lab technique inflates blood lead values by 20% = a bias. Confounding = uncontrolled for factor affecting results. Ex: You are doing a retrospective cohort study on chronic exposures to phosgene in laboratory workers and the incidence of lung cancer but you do not control for smoking. Smoking is a confounder
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  • A LITTLE TIP -KNOW HOW TO CALCULATE SENSITIVITY, SPECIFICITY, AND PREDICTIVE VALUES
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  • Sensitivity The likelihood of a test being positive if the condition is present Ex: Being under 16 yrs old has 100% sensitivity for the detection of childhood Pb poisoning. Good for screening (few false negatives (FN)) Sensitivity = True positives (TP)/(TP + FN) Sensitivity is often expressed as a % In example above if screen 100 individuals and 10 had Pb poisoning: Sens = 10/(10+0) = 10/10 = 1 (or 100%)
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  • Another example To determine the sensitivity of a terminal R in lead AVR for the detecting of Na + channel antagonist toxicity in all OD patients. Screen 1,000 EKGs of OD patients, 100 had ODd on Na + channel blockers and 80 had a terminal R wave (TPs). 50 had a terminal R wave but did not OD on these agents. TP = 80 FN = 20 Sens = TP/(TP + FN) = 80/(80 + 20) = 80/100 = 0.8 (80%)
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  • Specificity The likelihood of the unaffected individuals correctly having a negative test Test: using criteria of being under 16 for dx of childhood Pb poisoning. N = 100 10 with Pb poisoning - the other 90 are false positives (FP) Specificity = True neg (TN)/(TN + FP)= 0/0+90 = 0
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  • The second experiment Screen 1,000 EKGs of OD patients, 100 had ODd on Na + channel blockers and 80 had a terminal R wave. 50 others had a terminal R wave but did not OD on these agents (FPs). TN= 850 FP = 50 Sp = TN/(TN+FP) = 850/(850+50) = 850/900 =0.94 (94%)
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  • Comparison btw Sensitivity and specificity Both= True/(True + False) Sens = TP/(TP+FN) Specificity is the mirror image Spec = TN/(TN+FP) For both the trues in the numerator and denominator terms are the same. The other denominator term is the complete opposite
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  • Positive predicative value PPV = likelihood that the test will correctly Dx the condition Test: using criteria of being under 16 for dx of childhood Pb poisoning. N = 100 10 with Pb poisoning (TP) - the other 90 are false positives (FP) PPV = TP/(TP+FP) = 10/(10 + 90) = 0.1 So 10% PPV
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  • PPV the second experiment Screen 1,000 EKGs of OD patients, 100 had ODd on Na + channel blockers and 80 had a termina

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